Jhonathan Abreu
bc5d51806d
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* Normalize universe data frames Universe and (generically BaseDataCollection) data frames are not normalize and unpacked into a data frame, instead of just creating data frames with the universe lists within it * Fix unit tests and algorithms to expecte new universe dataframe format * Fixes * Add PandasConverter.DataFrameGenerator class * Pandas data frame generator class fixes * Add comments * Housekeeping * Add attributes to mark classes and properties for pandas processing * Improve pandas properties expanding Allow and handle duplicate names * Use PandasData generalization for Lean common data types * Add points time as column when converting base data collections to data frames * Cleanup and minor changes * Minor change * Pandas data to get type members on demand * Move Pandas helper classes to their own files * Minor changes * Add flatten argument to python history api This allows users to decide whether they want fully expanded dataframes for universe and other collection data types. Else, master behavior is kept * Adding missing changes to last commit * Update Pythonnet version to 2.0.40 * Add flattent argument to algorithm's OptionChain api * Minor changes * Housekeeping * Minor changes * Bug fix skipping data collection data points * Add comment * Set correct exchange time to OptionUniverse instances * Address peer review and cleanup * Cleanup * Minor changes
50 lines
2.2 KiB
Python
50 lines
2.2 KiB
Python
# QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
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# Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from AlgorithmImports import *
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from datetime import timedelta
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### <summary>
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### Regression algorithm illustrating the usage of the <see cref="QCAlgorithm.OptionChain(Symbol)"/> method
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### to get an option chain, which contains additional data besides the symbols, including prices, implied volatility and greeks.
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### It also shows how this data can be used to filter the contracts based on certain criteria.
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### </summary>
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class OptionChainFullDataRegressionAlgorithm(QCAlgorithm):
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def initialize(self):
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self.set_start_date(2015, 12, 24)
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self.set_end_date(2015, 12, 24)
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self.set_cash(100000)
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goog = self.add_equity("GOOG").symbol
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option_chain = self.option_chain(goog, flatten=True)
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# Demonstration using data frame:
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df = option_chain.data_frame
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# Get contracts expiring within 10 days, with an implied volatility greater than 0.5 and a delta less than 0.5
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contracts = df.loc[(df.expiry <= self.time + timedelta(days=10)) & (df.impliedvolatility > 0.5) & (df.delta < 0.5)]
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# Get the contract with the latest expiration date.
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# Note: the result of df.loc[] is a series, and its name is a tuple with a single element (contract symbol)
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self._option_contract = contracts.loc[contracts.expiry.idxmax()].name
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self.add_option_contract(self._option_contract)
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def on_data(self, data):
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# Do some trading with the selected contract for sample purposes
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if not self.portfolio.invested:
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self.market_order(self._option_contract, 1)
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else:
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self.liquidate()
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